To cover more range of practical applications, some NR-PCQA methods have been proposed as well. Chetouani \(et\)  \(al.\)  [1]} extract patch-wise hand-crafted features and use classical CNN models for quality regression. PQA-net [2]} utilizes multi-view projection for feature extraction. Zhang \(et\)  \(al.\)  [3]} use several statistical distributions to estimate quality-aware parameters from the geometry and color attributes' distributions. Fan \(et\)  \(al.\)  [4]} infer the visual quality of point clouds via the captured video sequences. Liu \(et\)  \(al.\)  [5]} employ an end-to-end sparse CNN for quality prediction. Yang \(et\)  \(al.\)  [6]} further transfer the quality information from natural images to help understand the point cloud rendering images' quality via domain adaptation. The mentioned methods are all based on single-modal information, thus failing to jointly incorporate the multi-modal quality information.
